Inductive Representation Learning on Large Graphs

TL;DR

GraphSAGE introduces an inductive learning framework for generating node embeddings in large graphs, enabling generalization to unseen nodes and graphs by aggregating local neighborhood features.

Contribution

The paper presents a novel inductive approach for node embedding that generalizes to unseen data, unlike previous transductive methods.

Findings

Outperforms baselines on inductive node classification tasks

Successfully generalizes to unseen nodes in evolving graphs

Effective on multi-graph protein interaction data

Abstract

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.